# tls: Weighted Total Least Square Regression. In DTA: Dynamic Transcriptome Analysis

## Description

Weigthed total least square regression according to Golub and Van Loan (1980) in SIAM J.Numer.Anal Vol 17 No.6.

## Usage

 `1` ```tls(formula, D = NULL, T = NULL, precision = .Machine\$double.eps) ```

## Arguments

 `formula` An object of class formula. `D` Diagonal weigth matrix. Default weights are set to 1. `T` Diagonal weigth matrix. Default weights are set to 1. `precision` Smallest possible numeric value on this machine (default).

## Value

`tls` returns a lm object.

## Author(s)

Sebastian Duemcke [email protected]

## References

Golub, G.H. and Van Loan, C.F. (1980). An analysis of the total least squares problem. SIAM J. Numer. Anal., 17:883-893.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ```f = 1.5 # true ratio a = rnorm(5000) b = f*a a = a + rnorm(5000,sd=0.5) b = b + rnorm(5000,sd=0.5) coeff.tls = coef(tls(b ~ a + 0)) coeff.lm1 = coef(lm(b ~ a + 0)) coeff.lm2 = 1/coef(lm(a ~ b + 0)) heatscatter(a,b) abline(0,coeff.lm1,col="red",pch=19,lwd=2) abline(0,coeff.lm2,col="orange",pch=19,lwd=2) abline(0,coeff.tls,col="green",pch=19,lwd=2) abline(0,f,col="grey",pch=19,lwd=2,lty=2) legend("topleft", c("Least-squares regr. (y ~ x + 0)", "Least-squares regr. (x ~ y + 0)", "Total Least-squares regr.", "True ratio"), col=c("red", "orange", "green", "grey"), lty=c(1,1,1,2), lwd=2) results = c(coeff.tls,coeff.lm1,coeff.lm2) names(results) = c("coeff.tls","coeff.lm1","coeff.lm2") print(results) ```

DTA documentation built on Nov. 1, 2018, 2:22 a.m.